These functions compute the point estimate and confidence interval for Cramer's V.

cramersV(x, y = NULL, digits = 2)

# S3 method for CramersV
print(x, digits = x$input$digits, ...)

confIntV(x, y = NULL, conf.level = 0.95, samples = 500, digits = 2,
  method = c("bootstrap", "fisher"), storeBootstrappingData = FALSE)

# S3 method for confIntV
print(x, digits = x$input$digits, ...)



Either a crosstable to analyse, or one of two vectors to use to generate that crosstable. The vector should be a factor, i.e. a categorical variable identified as such by the 'factor' class).


If x is a crosstable, y can (and should) be empty. If x is a vector, y must also be a vector.


Minimum number of digits after the decimal point to show in the result.

Any additional arguments are passed on to the print function.


Level of confidence for the confidence interval.


Number of samples to generate when bootstrapping.


Whether to use Fisher's Z or bootstrapping to compute the confidence interval.


Whether to store (or discard) the data generating during the bootstrapping procedure.


A point estimate or a confidence interval for Cramer's V, an effect size to describe the association between two categorical variables.


### Get confidence interval for Cramer's V ### Note that by using 'table', and so removing the raw data, inhibits ### bootstrapping, which could otherwise take a while. confIntV(table(infert$education, infert$induced));
#> Cramér's V 95% confidence interval (point estimate = .18): #> Using Fisher's z: [.06; .3]